Merging Structural and Reduced-Form Models for Forecasting

Jaime Martinez-Martin, Richard Morris, Luca Onorante, Fabio Massimo Piersanti
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Abstract

Recent economic crises have posed important challenges for forecasting. Models estimated pre-crisis may perform badly when normal economic relationships have been disrupted. Meanwhile, forecasting, especially in central banks, is increasingly based on a suite of models, following two main approaches: structural (DSGE) and reduced form. The challenge remains to identify which model – or combination of models – is likely to make better forecasts in a changing environment. We explore this issue by assessing the forecasting performance of combinations of a medium-scale DSGE model with standard reduced-form methods applied to the Spanish economy and a reference period that includes both the great recession and the sovereign debt crisis. Our findings suggest that: (i) the mean reverting properties of the DSGE model cause it to underestimate the growth of real variables following the inclusion of crisis episodes in the estimation period; (ii) despite this, reduced-form VARs benefit from the imposition of an economic prior from the structural model; but (iii) pooling information in the form of variables extracted from the structural model with (B)VAR methods does not improve forecast accuracy. By analysing the quantiles of the predictive distributions, we also provide evidence that merging models can help improve the forecast in a context including crisis episodes.
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合并结构模型和还原模型进行预测
最近的经济危机给预测带来了重大挑战。当正常的经济关系被打破时,危机前估计的模型可能会表现不佳。与此同时,预测工作,尤其是中央银行的预测工作,越来越多地基于一系列模型,主要有两种方法:结构模型(DSGE)和简化模型。在不断变化的环境中,确定哪种模型或模型组合可能做出更好的预测仍然是一项挑战。我们通过评估中等规模 DSGE 模型与标准简化形式方法组合的预测性能来探讨这一问题,该模型适用于西班牙经济和一个包括大衰退和主权债务危机的参照期。我们的研究结果表明(i) DSGE 模型的均值回复特性导致其在估算期纳入危机事件后低估了实际变量的增长;(ii) 尽管如此,简化形式 VAR 仍能从结构模型中施加的经济先验值中获益;但 (iii) 使用(B)VAR 方法汇集从结构模型中提取的变量信息并不能提高预测准确性。通过分析预测分布的数量级,我们还提供了在包括危机事件在内的情况下合并模型有助于改善预测的证据。
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